In this exercise we shall perform data analysis on olympic datasets¶

Data Import and Inspection¶

We shall perform data import and data inspection for performing analysis

In [2]:
import pandas as pd
In [3]:
sdata = pd.read_csv("summer.csv")
wdata = pd.read_csv("winter.csv")
dataDic = pd.read_csv("dictionary.csv")
In [4]:
sdata
Out[4]:
Year City Sport Discipline Athlete Country Gender Event Medal
0 1896 Athens Aquatics Swimming HAJOS, Alfred HUN Men 100M Freestyle Gold
1 1896 Athens Aquatics Swimming HERSCHMANN, Otto AUT Men 100M Freestyle Silver
2 1896 Athens Aquatics Swimming DRIVAS, Dimitrios GRE Men 100M Freestyle For Sailors Bronze
3 1896 Athens Aquatics Swimming MALOKINIS, Ioannis GRE Men 100M Freestyle For Sailors Gold
4 1896 Athens Aquatics Swimming CHASAPIS, Spiridon GRE Men 100M Freestyle For Sailors Silver
... ... ... ... ... ... ... ... ... ...
31160 2012 London Wrestling Wrestling Freestyle JANIKOWSKI, Damian POL Men Wg 84 KG Bronze
31161 2012 London Wrestling Wrestling Freestyle REZAEI, Ghasem Gholamreza IRI Men Wg 96 KG Gold
31162 2012 London Wrestling Wrestling Freestyle TOTROV, Rustam RUS Men Wg 96 KG Silver
31163 2012 London Wrestling Wrestling Freestyle ALEKSANYAN, Artur ARM Men Wg 96 KG Bronze
31164 2012 London Wrestling Wrestling Freestyle LIDBERG, Jimmy SWE Men Wg 96 KG Bronze

31165 rows × 9 columns

In [4]:
sdata.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 31165 entries, 0 to 31164
Data columns (total 9 columns):
 #   Column      Non-Null Count  Dtype 
---  ------      --------------  ----- 
 0   Year        31165 non-null  int64 
 1   City        31165 non-null  object
 2   Sport       31165 non-null  object
 3   Discipline  31165 non-null  object
 4   Athlete     31165 non-null  object
 5   Country     31161 non-null  object
 6   Gender      31165 non-null  object
 7   Event       31165 non-null  object
 8   Medal       31165 non-null  object
dtypes: int64(1), object(8)
memory usage: 2.1+ MB
In [5]:
wdata
Out[5]:
Year City Sport Discipline Athlete Country Gender Event Medal
0 1924 Chamonix Biathlon Biathlon BERTHET, G. FRA Men Military Patrol Bronze
1 1924 Chamonix Biathlon Biathlon MANDRILLON, C. FRA Men Military Patrol Bronze
2 1924 Chamonix Biathlon Biathlon MANDRILLON, Maurice FRA Men Military Patrol Bronze
3 1924 Chamonix Biathlon Biathlon VANDELLE, André FRA Men Military Patrol Bronze
4 1924 Chamonix Biathlon Biathlon AUFDENBLATTEN, Adolf SUI Men Military Patrol Gold
... ... ... ... ... ... ... ... ... ...
5765 2014 Sochi Skiing Snowboard JONES, Jenny GBR Women Slopestyle Bronze
5766 2014 Sochi Skiing Snowboard ANDERSON, Jamie USA Women Slopestyle Gold
5767 2014 Sochi Skiing Snowboard MALTAIS, Dominique CAN Women Snowboard Cross Silver
5768 2014 Sochi Skiing Snowboard SAMKOVA, Eva CZE Women Snowboard Cross Gold
5769 2014 Sochi Skiing Snowboard TRESPEUCH, Chloe FRA Women Snowboard Cross Bronze

5770 rows × 9 columns

In [6]:
wdata.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 5770 entries, 0 to 5769
Data columns (total 9 columns):
 #   Column      Non-Null Count  Dtype 
---  ------      --------------  ----- 
 0   Year        5770 non-null   int64 
 1   City        5770 non-null   object
 2   Sport       5770 non-null   object
 3   Discipline  5770 non-null   object
 4   Athlete     5770 non-null   object
 5   Country     5770 non-null   object
 6   Gender      5770 non-null   object
 7   Event       5770 non-null   object
 8   Medal       5770 non-null   object
dtypes: int64(1), object(8)
memory usage: 405.8+ KB
In [7]:
dataDic
Out[7]:
Country Code Population GDP per Capita
0 Afghanistan AFG 32526562.0 594.323081
1 Albania ALB 2889167.0 3945.217582
2 Algeria ALG 39666519.0 4206.031232
3 American Samoa* ASA 55538.0 NaN
4 Andorra AND 70473.0 NaN
... ... ... ... ...
196 Vietnam VIE 91703800.0 2111.138024
197 Virgin Islands* ISV 103574.0 NaN
198 Yemen YEM 26832215.0 1406.291651
199 Zambia ZAM 16211767.0 1304.879014
200 Zimbabwe ZIM 15602751.0 924.143819

201 rows × 4 columns

In [8]:
dataDic.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 201 entries, 0 to 200
Data columns (total 4 columns):
 #   Column          Non-Null Count  Dtype  
---  ------          --------------  -----  
 0   Country         201 non-null    object 
 1   Code            201 non-null    object 
 2   Population      196 non-null    float64
 3   GDP per Capita  176 non-null    float64
dtypes: float64(2), object(2)
memory usage: 6.4+ KB
  • Check Data dictionary null values
In [9]:
dataDic[dataDic.isnull().any(axis = 1)]
Out[9]:
Country Code Population GDP per Capita
3 American Samoa* ASA 55538.0 NaN
4 Andorra AND 70473.0 NaN
9 Aruba* ARU 103889.0 NaN
20 Bermuda* BER 65235.0 NaN
27 British Virgin Islands IVB 30117.0 NaN
36 Cayman Islands* CAY 59967.0 NaN
45 Cook Islands COK NaN NaN
49 Cuba CUB 11389562.0 NaN
61 Eritrea ERI NaN NaN
74 Guam GUM 169885.0 NaN
86 Iran IRI 79109272.0 NaN
96 Korea, North PRK 25155317.0 NaN
105 Libya LBA 6278438.0 NaN
106 Liechtenstein LIE 37531.0 NaN
116 Mauritania MTN 4067564.0 NaN
121 Monaco MON 37731.0 NaN
130 Netherlands Antilles* AHO NaN NaN
139 Palestine, Occupied Territories PLE NaN NaN
141 Papua New Guinea PNG 7619321.0 NaN
147 Puerto Rico* PUR 3474182.0 NaN
156 San Marino SMR 31781.0 NaN
176 Syria SYR 18502413.0 NaN
177 Taiwan TPE NaN NaN
195 Venezuela VEN 31108083.0 NaN
197 Virgin Islands* ISV 103574.0 NaN
In [ ]:

Merging and Concatenating¶

  1. Merge Summer and Winter (one row for each Medal awarded in any Olympic Games) and save the merged DataFrame in olympics.
  2. An additional column (e.g. "Edition") shall indicate the Edition -> Summer or Winter.
  3. Add the full Country name from dictionary to olympics (e.g. France for FRA).
In [5]:
pd.concat([sdata, wdata], axis = 0, keys = ["Summer", "Winter"],
          names = ["Edition"]).reset_index()
Out[5]:
Edition level_1 Year City Sport Discipline Athlete Country Gender Event Medal
0 Summer 0 1896 Athens Aquatics Swimming HAJOS, Alfred HUN Men 100M Freestyle Gold
1 Summer 1 1896 Athens Aquatics Swimming HERSCHMANN, Otto AUT Men 100M Freestyle Silver
2 Summer 2 1896 Athens Aquatics Swimming DRIVAS, Dimitrios GRE Men 100M Freestyle For Sailors Bronze
3 Summer 3 1896 Athens Aquatics Swimming MALOKINIS, Ioannis GRE Men 100M Freestyle For Sailors Gold
4 Summer 4 1896 Athens Aquatics Swimming CHASAPIS, Spiridon GRE Men 100M Freestyle For Sailors Silver
... ... ... ... ... ... ... ... ... ... ... ...
36930 Winter 5765 2014 Sochi Skiing Snowboard JONES, Jenny GBR Women Slopestyle Bronze
36931 Winter 5766 2014 Sochi Skiing Snowboard ANDERSON, Jamie USA Women Slopestyle Gold
36932 Winter 5767 2014 Sochi Skiing Snowboard MALTAIS, Dominique CAN Women Snowboard Cross Silver
36933 Winter 5768 2014 Sochi Skiing Snowboard SAMKOVA, Eva CZE Women Snowboard Cross Gold
36934 Winter 5769 2014 Sochi Skiing Snowboard TRESPEUCH, Chloe FRA Women Snowboard Cross Bronze

36935 rows × 11 columns

In [6]:
pd.concat([sdata, wdata], axis = 0, keys = ["Summer", "Winter"],
          names = ["Edition"]).reset_index().drop(columns = "level_1")
Out[6]:
Edition Year City Sport Discipline Athlete Country Gender Event Medal
0 Summer 1896 Athens Aquatics Swimming HAJOS, Alfred HUN Men 100M Freestyle Gold
1 Summer 1896 Athens Aquatics Swimming HERSCHMANN, Otto AUT Men 100M Freestyle Silver
2 Summer 1896 Athens Aquatics Swimming DRIVAS, Dimitrios GRE Men 100M Freestyle For Sailors Bronze
3 Summer 1896 Athens Aquatics Swimming MALOKINIS, Ioannis GRE Men 100M Freestyle For Sailors Gold
4 Summer 1896 Athens Aquatics Swimming CHASAPIS, Spiridon GRE Men 100M Freestyle For Sailors Silver
... ... ... ... ... ... ... ... ... ... ...
36930 Winter 2014 Sochi Skiing Snowboard JONES, Jenny GBR Women Slopestyle Bronze
36931 Winter 2014 Sochi Skiing Snowboard ANDERSON, Jamie USA Women Slopestyle Gold
36932 Winter 2014 Sochi Skiing Snowboard MALTAIS, Dominique CAN Women Snowboard Cross Silver
36933 Winter 2014 Sochi Skiing Snowboard SAMKOVA, Eva CZE Women Snowboard Cross Gold
36934 Winter 2014 Sochi Skiing Snowboard TRESPEUCH, Chloe FRA Women Snowboard Cross Bronze

36935 rows × 10 columns

In [13]:
olympics = pd.concat([sdata, wdata], axis = 0, keys = ["Summer", "Winter"],
                     names = ["Edition"]).reset_index().drop(columns = "level_1")
In [14]:
olympics
Out[14]:
Edition Year City Sport Discipline Athlete Country Gender Event Medal
0 Summer 1896 Athens Aquatics Swimming HAJOS, Alfred HUN Men 100M Freestyle Gold
1 Summer 1896 Athens Aquatics Swimming HERSCHMANN, Otto AUT Men 100M Freestyle Silver
2 Summer 1896 Athens Aquatics Swimming DRIVAS, Dimitrios GRE Men 100M Freestyle For Sailors Bronze
3 Summer 1896 Athens Aquatics Swimming MALOKINIS, Ioannis GRE Men 100M Freestyle For Sailors Gold
4 Summer 1896 Athens Aquatics Swimming CHASAPIS, Spiridon GRE Men 100M Freestyle For Sailors Silver
... ... ... ... ... ... ... ... ... ... ...
36930 Winter 2014 Sochi Skiing Snowboard JONES, Jenny GBR Women Slopestyle Bronze
36931 Winter 2014 Sochi Skiing Snowboard ANDERSON, Jamie USA Women Slopestyle Gold
36932 Winter 2014 Sochi Skiing Snowboard MALTAIS, Dominique CAN Women Snowboard Cross Silver
36933 Winter 2014 Sochi Skiing Snowboard SAMKOVA, Eva CZE Women Snowboard Cross Gold
36934 Winter 2014 Sochi Skiing Snowboard TRESPEUCH, Chloe FRA Women Snowboard Cross Bronze

36935 rows × 10 columns

In [15]:
dataDic.head()
Out[15]:
Country Code Population GDP per Capita
0 Afghanistan AFG 32526562.0 594.323081
1 Albania ALB 2889167.0 3945.217582
2 Algeria ALG 39666519.0 4206.031232
3 American Samoa* ASA 55538.0 NaN
4 Andorra AND 70473.0 NaN
In [19]:
olympics.merge(dataDic.iloc[:, :2], how = "left", 
               left_on = "Country", right_on = "Code").drop(columns = ["Code"])
Out[19]:
Edition Year City Sport Discipline Athlete Country_x Gender Event Medal Country_y
0 Summer 1896 Athens Aquatics Swimming HAJOS, Alfred HUN Men 100M Freestyle Gold Hungary
1 Summer 1896 Athens Aquatics Swimming HERSCHMANN, Otto AUT Men 100M Freestyle Silver Austria
2 Summer 1896 Athens Aquatics Swimming DRIVAS, Dimitrios GRE Men 100M Freestyle For Sailors Bronze Greece
3 Summer 1896 Athens Aquatics Swimming MALOKINIS, Ioannis GRE Men 100M Freestyle For Sailors Gold Greece
4 Summer 1896 Athens Aquatics Swimming CHASAPIS, Spiridon GRE Men 100M Freestyle For Sailors Silver Greece
... ... ... ... ... ... ... ... ... ... ... ...
36930 Winter 2014 Sochi Skiing Snowboard JONES, Jenny GBR Women Slopestyle Bronze United Kingdom
36931 Winter 2014 Sochi Skiing Snowboard ANDERSON, Jamie USA Women Slopestyle Gold United States
36932 Winter 2014 Sochi Skiing Snowboard MALTAIS, Dominique CAN Women Snowboard Cross Silver Canada
36933 Winter 2014 Sochi Skiing Snowboard SAMKOVA, Eva CZE Women Snowboard Cross Gold Czech Republic
36934 Winter 2014 Sochi Skiing Snowboard TRESPEUCH, Chloe FRA Women Snowboard Cross Bronze France

36935 rows × 11 columns

In [20]:
olympics = olympics.merge(dataDic.iloc[:, :2], how = "left", 
                          left_on = "Country", right_on = "Code").drop(columns = ["Code"])
In [21]:
olympics.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 36935 entries, 0 to 36934
Data columns (total 11 columns):
 #   Column      Non-Null Count  Dtype 
---  ------      --------------  ----- 
 0   Edition     36935 non-null  object
 1   Year        36935 non-null  int64 
 2   City        36935 non-null  object
 3   Sport       36935 non-null  object
 4   Discipline  36935 non-null  object
 5   Athlete     36935 non-null  object
 6   Country_x   36931 non-null  object
 7   Gender      36935 non-null  object
 8   Event       36935 non-null  object
 9   Medal       36935 non-null  object
 10  Country_y   30568 non-null  object
dtypes: int64(1), object(10)
memory usage: 3.4+ MB
In [22]:
olympics.reset_index(drop = True, inplace = True)
In [23]:
olympics
Out[23]:
Edition Year City Sport Discipline Athlete Country_x Gender Event Medal Country_y
0 Summer 1896 Athens Aquatics Swimming HAJOS, Alfred HUN Men 100M Freestyle Gold Hungary
1 Summer 1896 Athens Aquatics Swimming HERSCHMANN, Otto AUT Men 100M Freestyle Silver Austria
2 Summer 1896 Athens Aquatics Swimming DRIVAS, Dimitrios GRE Men 100M Freestyle For Sailors Bronze Greece
3 Summer 1896 Athens Aquatics Swimming MALOKINIS, Ioannis GRE Men 100M Freestyle For Sailors Gold Greece
4 Summer 1896 Athens Aquatics Swimming CHASAPIS, Spiridon GRE Men 100M Freestyle For Sailors Silver Greece
... ... ... ... ... ... ... ... ... ... ... ...
36930 Winter 2014 Sochi Skiing Snowboard JONES, Jenny GBR Women Slopestyle Bronze United Kingdom
36931 Winter 2014 Sochi Skiing Snowboard ANDERSON, Jamie USA Women Slopestyle Gold United States
36932 Winter 2014 Sochi Skiing Snowboard MALTAIS, Dominique CAN Women Snowboard Cross Silver Canada
36933 Winter 2014 Sochi Skiing Snowboard SAMKOVA, Eva CZE Women Snowboard Cross Gold Czech Republic
36934 Winter 2014 Sochi Skiing Snowboard TRESPEUCH, Chloe FRA Women Snowboard Cross Bronze France

36935 rows × 11 columns

In [ ]:

Data Cleaning (Part 1)¶

  1. If you haven´t done it yet: Assign appropriate Column Headers to Country Codes (e.g. "Code") and full Country Names (e.g. "Country").
  2. Remove Spaces from column headers in olympics and dictionary.
  3. For some Country Codes, there is no corresponding full Country Name available (e.g. for "URS") -> missing values in olympics. Identify these Country Codes and search the Web for the full Country Names. Replace missing values! (Alternatively, you can find a Solution for this at the end of this Notebook!)
In [24]:
olympics
Out[24]:
Edition Year City Sport Discipline Athlete Country_x Gender Event Medal Country_y
0 Summer 1896 Athens Aquatics Swimming HAJOS, Alfred HUN Men 100M Freestyle Gold Hungary
1 Summer 1896 Athens Aquatics Swimming HERSCHMANN, Otto AUT Men 100M Freestyle Silver Austria
2 Summer 1896 Athens Aquatics Swimming DRIVAS, Dimitrios GRE Men 100M Freestyle For Sailors Bronze Greece
3 Summer 1896 Athens Aquatics Swimming MALOKINIS, Ioannis GRE Men 100M Freestyle For Sailors Gold Greece
4 Summer 1896 Athens Aquatics Swimming CHASAPIS, Spiridon GRE Men 100M Freestyle For Sailors Silver Greece
... ... ... ... ... ... ... ... ... ... ... ...
36930 Winter 2014 Sochi Skiing Snowboard JONES, Jenny GBR Women Slopestyle Bronze United Kingdom
36931 Winter 2014 Sochi Skiing Snowboard ANDERSON, Jamie USA Women Slopestyle Gold United States
36932 Winter 2014 Sochi Skiing Snowboard MALTAIS, Dominique CAN Women Snowboard Cross Silver Canada
36933 Winter 2014 Sochi Skiing Snowboard SAMKOVA, Eva CZE Women Snowboard Cross Gold Czech Republic
36934 Winter 2014 Sochi Skiing Snowboard TRESPEUCH, Chloe FRA Women Snowboard Cross Bronze France

36935 rows × 11 columns

In [25]:
olympics.rename(columns = {"Country_x":"Code", "Country_y": "Country"}, inplace = True)
In [26]:
dataDic
Out[26]:
Country Code Population GDP per Capita
0 Afghanistan AFG 32526562.0 594.323081
1 Albania ALB 2889167.0 3945.217582
2 Algeria ALG 39666519.0 4206.031232
3 American Samoa* ASA 55538.0 NaN
4 Andorra AND 70473.0 NaN
... ... ... ... ...
196 Vietnam VIE 91703800.0 2111.138024
197 Virgin Islands* ISV 103574.0 NaN
198 Yemen YEM 26832215.0 1406.291651
199 Zambia ZAM 16211767.0 1304.879014
200 Zimbabwe ZIM 15602751.0 924.143819

201 rows × 4 columns

In [27]:
dataDic.rename(columns = {"GDP per Capita":"GDP"}, inplace= True)
In [28]:
olympics.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 36935 entries, 0 to 36934
Data columns (total 11 columns):
 #   Column      Non-Null Count  Dtype 
---  ------      --------------  ----- 
 0   Edition     36935 non-null  object
 1   Year        36935 non-null  int64 
 2   City        36935 non-null  object
 3   Sport       36935 non-null  object
 4   Discipline  36935 non-null  object
 5   Athlete     36935 non-null  object
 6   Code        36931 non-null  object
 7   Gender      36935 non-null  object
 8   Event       36935 non-null  object
 9   Medal       36935 non-null  object
 10  Country     30568 non-null  object
dtypes: int64(1), object(10)
memory usage: 3.1+ MB
In [29]:
olympics.loc[olympics.Country.isnull()]
Out[29]:
Edition Year City Sport Discipline Athlete Code Gender Event Medal Country
132 Summer 1896 Athens Tennis Tennis FLACK, Edwin ZZX Men Doubles Bronze NaN
133 Summer 1896 Athens Tennis Tennis ROBERTSON, George Stuart ZZX Men Doubles Bronze NaN
134 Summer 1896 Athens Tennis Tennis BOLAND, John ZZX Men Doubles Gold NaN
135 Summer 1896 Athens Tennis Tennis TRAUN, Friedrich ZZX Men Doubles Gold NaN
136 Summer 1896 Athens Tennis Tennis KASDAGLIS, Dionysios ZZX Men Doubles Silver NaN
... ... ... ... ... ... ... ... ... ... ... ...
33961 Winter 1992 Albertville Skiing Freestyle Skiing KOZHEVNIKOVA, Yelizaveta EUN Women Moguls Silver NaN
33977 Winter 1992 Albertville Skiing Ski Jumping GODER, Tomas TCH Men K120 Team (90M) Bronze NaN
33978 Winter 1992 Albertville Skiing Ski Jumping JEZ, Frantisek TCH Men K120 Team (90M) Bronze NaN
33979 Winter 1992 Albertville Skiing Ski Jumping PARMA, Jiri TCH Men K120 Team (90M) Bronze NaN
33980 Winter 1992 Albertville Skiing Ski Jumping SAKALA, Jaroslav TCH Men K120 Team (90M) Bronze NaN

6367 rows × 11 columns

In [30]:
missing_C_index = olympics.loc[olympics.Country.isnull()].index
missing_C_index
Out[30]:
Int64Index([  132,   133,   134,   135,   136,   137,   257,   258,   259,
              260,
            ...
            33939, 33947, 33949, 33953, 33954, 33961, 33977, 33978, 33979,
            33980],
           dtype='int64', length=6367)
In [31]:
olympics.loc[olympics.Country.isnull()].Code.value_counts()
Out[31]:
URS    2489
GDR     987
ROU     642
FRG     584
TCH     487
YUG     442
EUN     283
EUA     281
ZZX      48
SRB      31
ANZ      29
RU1      17
MNE      14
TTO      10
BOH       7
BWI       5
SGP       4
IOP       3
Name: Code, dtype: int64
In [32]:
old_c = olympics.loc[olympics.Country.isnull(), :].Code.value_counts().index
old_c
Out[32]:
Index(['URS', 'GDR', 'ROU', 'FRG', 'TCH', 'YUG', 'EUN', 'EUA', 'ZZX', 'SRB',
       'ANZ', 'RU1', 'MNE', 'TTO', 'BOH', 'BWI', 'SGP', 'IOP'],
      dtype='object')
In [33]:
mapper = pd.Series(index=old_c, name = "Country", data = ["Soviet Union", "East Germany", "Romania", "West Germany", "Czechoslovakia",
                               "Yugoslavia", "Unified Team", "Unified Team of Germany", "Mixed teams", "Serbia",
                              "Australasia", "Russian Empire", "Montenegro", "Trinidad and Tobago", "Bohemia", 
                              "West Indies Federation", "Singapore", "Independent Olympic Participants"])
In [34]:
mapper
Out[34]:
URS                        Soviet Union
GDR                        East Germany
ROU                             Romania
FRG                        West Germany
TCH                      Czechoslovakia
YUG                          Yugoslavia
EUN                        Unified Team
EUA             Unified Team of Germany
ZZX                         Mixed teams
SRB                              Serbia
ANZ                         Australasia
RU1                      Russian Empire
MNE                          Montenegro
TTO                 Trinidad and Tobago
BOH                             Bohemia
BWI              West Indies Federation
SGP                           Singapore
IOP    Independent Olympic Participants
Name: Country, dtype: object
In [35]:
olympics.loc[missing_C_index, "Code"].map(mapper)
Out[35]:
132         Mixed teams
133         Mixed teams
134         Mixed teams
135         Mixed teams
136         Mixed teams
              ...      
33961      Unified Team
33977    Czechoslovakia
33978    Czechoslovakia
33979    Czechoslovakia
33980    Czechoslovakia
Name: Code, Length: 6367, dtype: object
In [36]:
olympics.Country.fillna(olympics.Code.map(mapper), inplace = True)
In [37]:
olympics.loc[missing_C_index]
Out[37]:
Edition Year City Sport Discipline Athlete Code Gender Event Medal Country
132 Summer 1896 Athens Tennis Tennis FLACK, Edwin ZZX Men Doubles Bronze Mixed teams
133 Summer 1896 Athens Tennis Tennis ROBERTSON, George Stuart ZZX Men Doubles Bronze Mixed teams
134 Summer 1896 Athens Tennis Tennis BOLAND, John ZZX Men Doubles Gold Mixed teams
135 Summer 1896 Athens Tennis Tennis TRAUN, Friedrich ZZX Men Doubles Gold Mixed teams
136 Summer 1896 Athens Tennis Tennis KASDAGLIS, Dionysios ZZX Men Doubles Silver Mixed teams
... ... ... ... ... ... ... ... ... ... ... ...
33961 Winter 1992 Albertville Skiing Freestyle Skiing KOZHEVNIKOVA, Yelizaveta EUN Women Moguls Silver Unified Team
33977 Winter 1992 Albertville Skiing Ski Jumping GODER, Tomas TCH Men K120 Team (90M) Bronze Czechoslovakia
33978 Winter 1992 Albertville Skiing Ski Jumping JEZ, Frantisek TCH Men K120 Team (90M) Bronze Czechoslovakia
33979 Winter 1992 Albertville Skiing Ski Jumping PARMA, Jiri TCH Men K120 Team (90M) Bronze Czechoslovakia
33980 Winter 1992 Albertville Skiing Ski Jumping SAKALA, Jaroslav TCH Men K120 Team (90M) Bronze Czechoslovakia

6367 rows × 11 columns

Data Cleaning (Part 2)¶

  1. Remove rows from olympics where the Country code is unknown. (Make sure you reset the Index -> RangeIndex)
  2. Convert the column Medal into an ordered Categorical column ("Bronze" < "Silver" < "Gold")
In [38]:
olympics.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 36935 entries, 0 to 36934
Data columns (total 11 columns):
 #   Column      Non-Null Count  Dtype 
---  ------      --------------  ----- 
 0   Edition     36935 non-null  object
 1   Year        36935 non-null  int64 
 2   City        36935 non-null  object
 3   Sport       36935 non-null  object
 4   Discipline  36935 non-null  object
 5   Athlete     36935 non-null  object
 6   Code        36931 non-null  object
 7   Gender      36935 non-null  object
 8   Event       36935 non-null  object
 9   Medal       36935 non-null  object
 10  Country     36931 non-null  object
dtypes: int64(1), object(10)
memory usage: 3.1+ MB
In [39]:
olympics[olympics.Code.isna()]
Out[39]:
Edition Year City Sport Discipline Athlete Code Gender Event Medal Country
29603 Summer 2012 London Athletics Athletics Pending NaN Women 1500M Gold NaN
31072 Summer 2012 London Weightlifting Weightlifting Pending NaN Women 63KG Gold NaN
31091 Summer 2012 London Weightlifting Weightlifting Pending NaN Men 94KG Silver NaN
31110 Summer 2012 London Wrestling Wrestling Freestyle KUDUKHOV, Besik NaN Men Wf 60 KG Silver NaN
In [40]:
olympics.dropna(subset = ["Code"], inplace = True)
In [41]:
olympics.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 36931 entries, 0 to 36934
Data columns (total 11 columns):
 #   Column      Non-Null Count  Dtype 
---  ------      --------------  ----- 
 0   Edition     36931 non-null  object
 1   Year        36931 non-null  int64 
 2   City        36931 non-null  object
 3   Sport       36931 non-null  object
 4   Discipline  36931 non-null  object
 5   Athlete     36931 non-null  object
 6   Code        36931 non-null  object
 7   Gender      36931 non-null  object
 8   Event       36931 non-null  object
 9   Medal       36931 non-null  object
 10  Country     36931 non-null  object
dtypes: int64(1), object(10)
memory usage: 3.4+ MB
In [42]:
olympics.reset_index(drop = True, inplace = True)
In [43]:
olympics.nunique()
Out[43]:
Edition           2
Year             33
City             41
Sport            48
Discipline       80
Athlete       26494
Code            148
Gender            2
Event           733
Medal             3
Country         145
dtype: int64
In [44]:
olympics.Medal = olympics.Medal.astype("category")
In [45]:
olympics.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 36931 entries, 0 to 36930
Data columns (total 11 columns):
 #   Column      Non-Null Count  Dtype   
---  ------      --------------  -----   
 0   Edition     36931 non-null  object  
 1   Year        36931 non-null  int64   
 2   City        36931 non-null  object  
 3   Sport       36931 non-null  object  
 4   Discipline  36931 non-null  object  
 5   Athlete     36931 non-null  object  
 6   Code        36931 non-null  object  
 7   Gender      36931 non-null  object  
 8   Event       36931 non-null  object  
 9   Medal       36931 non-null  category
 10  Country     36931 non-null  object  
dtypes: category(1), int64(1), object(9)
memory usage: 2.9+ MB
In [46]:
olympics.Medal.sort_values()
Out[46]:
36930    Bronze
25565    Bronze
25562    Bronze
25559    Bronze
11084    Bronze
          ...  
10811    Silver
10810    Silver
10809    Silver
25834    Silver
18465    Silver
Name: Medal, Length: 36931, dtype: category
Categories (3, object): ['Bronze', 'Gold', 'Silver']
In [47]:
olympics.Medal.cat.set_categories(["Bronze", "Silver", "Gold"], ordered = True, inplace = True)

C:\Users\Jayanth\AppData\Local\Temp\ipykernel_18964\1904861286.py:1: FutureWarning: The `inplace` parameter in pandas.Categorical.set_categories is deprecated and will be removed in a future version. Removing unused categories will always return a new Categorical object.
  olympics.Medal.cat.set_categories(["Bronze", "Silver", "Gold"], ordered = True, inplace = True)
In [48]:
olympics.Medal.sort_values()
Out[48]:
36930    Bronze
8916     Bronze
15339    Bronze
27698    Bronze
8919     Bronze
          ...  
25833      Gold
10807      Gold
10806      Gold
10804      Gold
0          Gold
Name: Medal, Length: 36931, dtype: category
Categories (3, object): ['Bronze' < 'Silver' < 'Gold']

What are the most successful countries of all times?¶

For the next questions, use Seaborn plots.

  1. What are the Top 10 Countries by total medals?
  2. Split the total medals of Top 10 Countries into Summer / Winter. Are there typical Summer/Winter Games Countries?
  3. Split the total medals of Top 10 Countries into Gold, Silver, Bronze.
In [49]:
import matplotlib.pyplot as plt
import seaborn as sns
In [50]:
olympics
Out[50]:
Edition Year City Sport Discipline Athlete Code Gender Event Medal Country
0 Summer 1896 Athens Aquatics Swimming HAJOS, Alfred HUN Men 100M Freestyle Gold Hungary
1 Summer 1896 Athens Aquatics Swimming HERSCHMANN, Otto AUT Men 100M Freestyle Silver Austria
2 Summer 1896 Athens Aquatics Swimming DRIVAS, Dimitrios GRE Men 100M Freestyle For Sailors Bronze Greece
3 Summer 1896 Athens Aquatics Swimming MALOKINIS, Ioannis GRE Men 100M Freestyle For Sailors Gold Greece
4 Summer 1896 Athens Aquatics Swimming CHASAPIS, Spiridon GRE Men 100M Freestyle For Sailors Silver Greece
... ... ... ... ... ... ... ... ... ... ... ...
36926 Winter 2014 Sochi Skiing Snowboard JONES, Jenny GBR Women Slopestyle Bronze United Kingdom
36927 Winter 2014 Sochi Skiing Snowboard ANDERSON, Jamie USA Women Slopestyle Gold United States
36928 Winter 2014 Sochi Skiing Snowboard MALTAIS, Dominique CAN Women Snowboard Cross Silver Canada
36929 Winter 2014 Sochi Skiing Snowboard SAMKOVA, Eva CZE Women Snowboard Cross Gold Czech Republic
36930 Winter 2014 Sochi Skiing Snowboard TRESPEUCH, Chloe FRA Women Snowboard Cross Bronze France

36931 rows × 11 columns

In [51]:
olympics.Country.value_counts()
Out[51]:
United States            5238
Soviet Union             2489
United Kingdom           1799
Germany                  1665
France                   1548
                         ... 
Virgin Islands*             1
Guyana                      1
Netherlands Antilles*       1
Iraq                        1
Bermuda*                    1
Name: Country, Length: 145, dtype: int64
In [52]:
top_10 = olympics.Country.value_counts().head(10)
top_10
Out[52]:
United States     5238
Soviet Union      2489
United Kingdom    1799
Germany           1665
France            1548
Italy             1488
Sweden            1477
Canada            1274
Australia         1204
Hungary           1091
Name: Country, dtype: int64
In [53]:
top_10.plot(kind = "bar", fontsize = 15, figsize=(12,8))
plt.title("Top 10 Countries by Medals", fontsize = 15)
plt.ylabel("Medals", fontsize = 14)
plt.show()
In [54]:
olympics_10 = olympics[olympics.Country.isin(top_10.index)]
olympics_10
Out[54]:
Edition Year City Sport Discipline Athlete Code Gender Event Medal Country
0 Summer 1896 Athens Aquatics Swimming HAJOS, Alfred HUN Men 100M Freestyle Gold Hungary
6 Summer 1896 Athens Aquatics Swimming HAJOS, Alfred HUN Men 1200M Freestyle Gold Hungary
11 Summer 1896 Athens Athletics Athletics LANE, Francis USA Men 100M Bronze United States
12 Summer 1896 Athens Athletics Athletics SZOKOLYI, Alajos HUN Men 100M Bronze Hungary
13 Summer 1896 Athens Athletics Athletics BURKE, Thomas USA Men 100M Gold United States
... ... ... ... ... ... ... ... ... ... ... ...
36924 Winter 2014 Sochi Skiing Snowboard KOBER, Amelie GER Women Parallel Slalom Bronze Germany
36926 Winter 2014 Sochi Skiing Snowboard JONES, Jenny GBR Women Slopestyle Bronze United Kingdom
36927 Winter 2014 Sochi Skiing Snowboard ANDERSON, Jamie USA Women Slopestyle Gold United States
36928 Winter 2014 Sochi Skiing Snowboard MALTAIS, Dominique CAN Women Snowboard Cross Silver Canada
36930 Winter 2014 Sochi Skiing Snowboard TRESPEUCH, Chloe FRA Women Snowboard Cross Bronze France

19273 rows × 11 columns

In [55]:
plt.figure(figsize=(20,10))
sns.set(font_scale=1.5, palette= "dark")
sns.countplot(data = olympics_10, x = "Country", order = top_10.index)
plt.title("Top 10 Countries by Medals", fontsize = 20)
plt.show()
In [56]:
plt.figure(figsize=(20,10))
sns.set(font_scale=1.5, palette= "dark")
sns.countplot(data = olympics_10, x = "Country", hue = "Edition", order = top_10.index)
plt.title("Top 10 Countries by Medals", fontsize = 20)
plt.show()
In [57]:
plt.figure(figsize=(20,10))
sns.set(font_scale=1.5, palette= "dark")
sns.countplot(data = olympics_10, x = "Edition", hue = "Country", hue_order = top_10.index)
plt.title("Top 10 Countries by Medals", fontsize = 20)
plt.show()
In [58]:
plt.figure(figsize=(20,10))
sns.set(font_scale=1.5, palette= "dark")
sns.countplot(data = olympics_10, x = "Country", hue = "Medal", order = top_10.index,
              hue_order = ["Gold", "Silver", "Bronze"], palette = ["gold", "silver", "brown"])
plt.title("Top 10 Countries by Medals", fontsize = 20)
plt.show()
In [59]:
plt.figure(figsize=(20,10))
sns.set(font_scale=1.5, palette= "dark")
sns.countplot(data = olympics_10, x = "Medal", hue = "Country", 
              order = ["Gold", "Silver", "Bronze"], hue_order= top_10.index)
plt.title("Top 10 Countries by Medals", fontsize = 20)
plt.show()

Do GDP, Population and Politics matter?¶

  1. Create the following aggregated and merged DataFrame with Top 50 Countries (you can see an excerpt with the first 12 Countries). The Column Total_Games shows the number of Participations (as an approximation: determine the number of Editions where Countries have won at least one medal).

image.png

  1. Convert the absolute values in the DataFrame into ranks and save the ranks DataFrame in new variable (see screenshot). Ranks are more meaningful than absolute numbers.

image.png

In [60]:
olympics
Out[60]:
Edition Year City Sport Discipline Athlete Code Gender Event Medal Country
0 Summer 1896 Athens Aquatics Swimming HAJOS, Alfred HUN Men 100M Freestyle Gold Hungary
1 Summer 1896 Athens Aquatics Swimming HERSCHMANN, Otto AUT Men 100M Freestyle Silver Austria
2 Summer 1896 Athens Aquatics Swimming DRIVAS, Dimitrios GRE Men 100M Freestyle For Sailors Bronze Greece
3 Summer 1896 Athens Aquatics Swimming MALOKINIS, Ioannis GRE Men 100M Freestyle For Sailors Gold Greece
4 Summer 1896 Athens Aquatics Swimming CHASAPIS, Spiridon GRE Men 100M Freestyle For Sailors Silver Greece
... ... ... ... ... ... ... ... ... ... ... ...
36926 Winter 2014 Sochi Skiing Snowboard JONES, Jenny GBR Women Slopestyle Bronze United Kingdom
36927 Winter 2014 Sochi Skiing Snowboard ANDERSON, Jamie USA Women Slopestyle Gold United States
36928 Winter 2014 Sochi Skiing Snowboard MALTAIS, Dominique CAN Women Snowboard Cross Silver Canada
36929 Winter 2014 Sochi Skiing Snowboard SAMKOVA, Eva CZE Women Snowboard Cross Gold Czech Republic
36930 Winter 2014 Sochi Skiing Snowboard TRESPEUCH, Chloe FRA Women Snowboard Cross Bronze France

36931 rows × 11 columns

In [61]:
medals_per_country = pd.crosstab(index = olympics.Country, columns = olympics.Medal, 
                                 margins = True, margins_name= "Total")
medals_per_country
Out[61]:
Medal Bronze Silver Gold Total
Country
Afghanistan 2 0 0 2
Algeria 8 2 5 15
Argentina 91 99 69 259
Armenia 8 2 1 11
Australasia 5 4 20 29
... ... ... ... ...
West Indies Federation 5 0 0 5
Yugoslavia 119 180 143 442
Zambia 1 1 0 2
Zimbabwe 1 4 18 23
Total 12288 12238 12405 36931

146 rows × 4 columns

In [62]:
medals_per_country.drop("Total", axis = 0, inplace = True)
In [63]:
medals_per_country
Out[63]:
Medal Bronze Silver Gold Total
Country
Afghanistan 2 0 0 2
Algeria 8 2 5 15
Argentina 91 99 69 259
Armenia 8 2 1 11
Australasia 5 4 20 29
... ... ... ... ...
West Germany 227 191 166 584
West Indies Federation 5 0 0 5
Yugoslavia 119 180 143 442
Zambia 1 1 0 2
Zimbabwe 1 4 18 23

145 rows × 4 columns

In [64]:
medals_per_country = medals_per_country[["Total", "Gold", "Silver", "Bronze"]].sort_values(by = ["Total", "Gold", "Silver", "Bronze"], ascending = False)
In [65]:
medals_per_country.head(50)
Out[65]:
Medal Total Gold Silver Bronze
Country
United States 5238 2402 1571 1265
Soviet Union 2489 1088 724 677
United Kingdom 1799 580 632 587
Germany 1665 589 504 572
France 1548 444 526 578
Italy 1488 534 473 481
Sweden 1477 476 496 505
Canada 1274 470 435 369
Australia 1204 317 408 479
Hungary 1091 412 320 359
Russia 1031 333 328 370
Norway 1011 368 371 272
East Germany 987 387 321 279
Netherlands 973 275 317 381
Finland 890 190 267 433
China 889 306 326 257
Japan 851 230 294 327
Switzerland 665 151 244 270
Romania 642 157 195 290
Korea, South 616 209 230 177
West Germany 584 166 191 227
Poland 538 112 184 242
Denmark 512 150 202 160
Czechoslovakia 487 82 224 181
Spain 444 99 227 118
Yugoslavia 442 143 180 119
Brazil 431 73 173 185
Austria 426 100 179 147
Belgium 424 93 172 159
Cuba 410 165 129 116
Bulgaria 339 54 145 140
Unified Team 283 128 75 80
Unified Team of Germany 281 76 108 97
Argentina 259 69 99 91
New Zealand 191 85 31 75
India 184 128 18 38
Ukraine 184 46 43 95
Greece 148 34 62 52
Czech Republic 131 42 34 55
Belarus 128 23 41 64
Jamaica 127 31 49 47
Croatia 125 50 40 35
Pakistan 121 42 45 34
Mexico 106 32 23 51
South Africa 106 30 39 37
Kenya 93 28 35 30
Turkey 86 38 25 23
Nigeria 84 19 38 27
Uruguay 76 44 2 30
Iran 61 16 21 24
In [66]:
dataDic
Out[66]:
Country Code Population GDP
0 Afghanistan AFG 32526562.0 594.323081
1 Albania ALB 2889167.0 3945.217582
2 Algeria ALG 39666519.0 4206.031232
3 American Samoa* ASA 55538.0 NaN
4 Andorra AND 70473.0 NaN
... ... ... ... ...
196 Vietnam VIE 91703800.0 2111.138024
197 Virgin Islands* ISV 103574.0 NaN
198 Yemen YEM 26832215.0 1406.291651
199 Zambia ZAM 16211767.0 1304.879014
200 Zimbabwe ZIM 15602751.0 924.143819

201 rows × 4 columns

In [68]:
medals_per_country = medals_per_country.merge(dataDic, how = "left", left_index = True, 
                            right_on = "Country").drop(columns = ["Code"]).set_index("Country")
In [69]:
medals_per_country
Out[69]:
Total Gold Silver Bronze Population GDP
Country
United States 5238 2402 1571 1265 321418820.0 56115.718426
Soviet Union 2489 1088 724 677 NaN NaN
United Kingdom 1799 580 632 587 65138232.0 43875.969614
Germany 1665 589 504 572 81413145.0 41313.313995
France 1548 444 526 578 66808385.0 36205.568102
... ... ... ... ... ... ...
Iraq 1 0 0 1 36423395.0 4943.760388
Macedonia 1 0 0 1 2078453.0 4852.657848
Mauritius 1 0 0 1 1262605.0 9252.110724
Niger 1 0 0 1 19899120.0 358.958152
Togo 1 0 0 1 7304578.0 559.635877

145 rows × 6 columns

In [70]:
olympics.nunique()
Out[70]:
Edition           2
Year             33
City             41
Sport            48
Discipline       80
Athlete       26494
Code            148
Gender            2
Event           733
Medal             3
Country         145
dtype: int64

Lambda or anonymous function in python¶

In [16]:
y = lambda x : x **2
y
Out[16]:
<function __main__.<lambda>(x)>
In [20]:
list_x = [2,3,4,5,6,7,8]
z = map(y,list_x)
print('---',list(z))

w = map(lambda x : x **2,list_x) # One function to another function - higher order function (HOF)
print('---',list(w))

--- [4, 9, 16, 25, 36, 49, 64]
--- [4, 9, 16, 25, 36, 49, 64]
In [71]:
olympics["Games"] = olympics.apply(lambda x: str(x.Year) + " " + x.City, axis = 1)
In [72]:
olympics
Out[72]:
Edition Year City Sport Discipline Athlete Code Gender Event Medal Country Games
0 Summer 1896 Athens Aquatics Swimming HAJOS, Alfred HUN Men 100M Freestyle Gold Hungary 1896 Athens
1 Summer 1896 Athens Aquatics Swimming HERSCHMANN, Otto AUT Men 100M Freestyle Silver Austria 1896 Athens
2 Summer 1896 Athens Aquatics Swimming DRIVAS, Dimitrios GRE Men 100M Freestyle For Sailors Bronze Greece 1896 Athens
3 Summer 1896 Athens Aquatics Swimming MALOKINIS, Ioannis GRE Men 100M Freestyle For Sailors Gold Greece 1896 Athens
4 Summer 1896 Athens Aquatics Swimming CHASAPIS, Spiridon GRE Men 100M Freestyle For Sailors Silver Greece 1896 Athens
... ... ... ... ... ... ... ... ... ... ... ... ...
36926 Winter 2014 Sochi Skiing Snowboard JONES, Jenny GBR Women Slopestyle Bronze United Kingdom 2014 Sochi
36927 Winter 2014 Sochi Skiing Snowboard ANDERSON, Jamie USA Women Slopestyle Gold United States 2014 Sochi
36928 Winter 2014 Sochi Skiing Snowboard MALTAIS, Dominique CAN Women Snowboard Cross Silver Canada 2014 Sochi
36929 Winter 2014 Sochi Skiing Snowboard SAMKOVA, Eva CZE Women Snowboard Cross Gold Czech Republic 2014 Sochi
36930 Winter 2014 Sochi Skiing Snowboard TRESPEUCH, Chloe FRA Women Snowboard Cross Bronze France 2014 Sochi

36931 rows × 12 columns

In [73]:
olympics.Games.nunique()
Out[73]:
49
In [74]:
olympics.Games.value_counts()
Out[74]:
2008 Beijing                   2042
2000 Sydney                    2015
2004 Athens                    1998
2012 London                    1945
1996 Atlanta                   1859
1992 Barcelona                 1705
1988 Seoul                     1546
1984 Los Angeles               1459
1980 Moscow                    1387
1976 Montreal                  1305
1920 Antwerp                   1298
1972 Munich                    1185
1968 Mexico                    1031
1964 Tokyo                     1010
1952 Helsinki                   889
1956 Melbourne / Stockholm      885
1912 Stockholm                  885
1924 Paris                      884
1960 Rome                       882
1936 Berlin                     875
1948 London                     814
1908 London                     804
1928 Amsterdam                  710
1932 Los Angeles                615
2014 Sochi                      612
2006 Turin                      531
2010 Vancouver                  529
1900 Paris                      512
2002 Salt Lake City             481
1904 St Louis                   470
1998 Nagano                     447
1994 Lillehammer                343
1992 Albertville                325
1988 Calgary                    264
1984 Sarajevo                   222
1980 Lake Placid                218
1976 Innsbruck                  210
1972 Sapporo                    200
1968 Grenoble                   199
1964 Innsbruck                  185
1896 Athens                     151
1956 Cortina d'Ampezzo          150
1960 Squaw Valley               147
1948 St.Moritz                  140
1952 Oslo                       136
1924 Chamonix                   118
1932 Lake Placid                116
1936 Garmisch Partenkirchen     108
1928 St.Moritz                   89
Name: Games, dtype: int64
In [75]:
olympics.groupby("Country").apply(lambda x: x.Games.nunique())
Out[75]:
Country
Afghanistan                2
Algeria                    6
Argentina                 18
Armenia                    4
Australasia                2
                          ..
West Germany              12
West Indies Federation     1
Yugoslavia                18
Zambia                     2
Zimbabwe                   3
Length: 145, dtype: int64
In [76]:
medals_per_country["Total_Games"] = olympics.groupby("Country").apply(lambda x: x.Games.nunique())
In [77]:
medals_per_country.head(50)
Out[77]:
Total Gold Silver Bronze Population GDP Total_Games
Country
United States 5238 2402 1571 1265 3.214188e+08 56115.718426 48
Soviet Union 2489 1088 724 677 NaN NaN 18
United Kingdom 1799 580 632 587 6.513823e+07 43875.969614 42
Germany 1665 589 504 572 8.141314e+07 41313.313995 25
France 1548 444 526 578 6.680838e+07 36205.568102 47
Italy 1488 534 473 481 6.080208e+07 29957.804315 43
Sweden 1477 476 496 505 9.798871e+06 50579.673649 47
Canada 1274 470 435 369 3.585177e+07 43248.529909 47
Australia 1204 317 408 479 2.378117e+07 56310.962993 30
Hungary 1091 412 320 359 9.844686e+06 12363.543460 31
Russia 1031 333 328 370 1.440968e+08 9092.580536 11
Norway 1011 368 371 272 5.195921e+06 74400.369777 44
East Germany 987 387 321 279 NaN NaN 11
Netherlands 973 275 317 381 1.693652e+07 44299.768085 39
Finland 890 190 267 433 5.482013e+06 42311.036231 46
China 889 306 326 257 1.371220e+09 8027.683810 15
Japan 851 230 294 327 1.269585e+08 32477.215145 32
Switzerland 665 151 244 270 8.286976e+06 80945.079219 46
Romania 642 157 195 290 1.983239e+07 8972.922518 19
Korea, South 616 209 230 177 5.061704e+07 27221.524051 22
West Germany 584 166 191 227 NaN NaN 12
Poland 538 112 184 242 3.799949e+07 12554.547554 27
Denmark 512 150 202 160 5.676002e+06 51989.293471 27
Czechoslovakia 487 82 224 181 NaN NaN 27
Spain 444 99 227 118 4.641827e+07 25831.582305 20
Yugoslavia 442 143 180 119 NaN NaN 18
Brazil 431 73 173 185 2.078475e+08 8538.589975 18
Austria 426 100 179 147 8.611088e+06 43774.985174 46
Belgium 424 93 172 159 1.128572e+07 40324.027766 28
Cuba 410 165 129 116 1.138956e+07 NaN 14
Bulgaria 339 54 145 140 7.177991e+06 6993.477360 19
Unified Team 283 128 75 80 NaN NaN 2
Unified Team of Germany 281 76 108 97 NaN NaN 6
Argentina 259 69 99 91 4.341676e+07 13431.878340 18
New Zealand 191 85 31 75 4.595700e+06 37807.967276 21
India 184 128 18 38 1.311051e+09 1598.259034 17
Ukraine 184 46 43 95 4.519820e+07 2114.954716 9
Greece 148 34 62 52 1.082373e+07 18002.230578 18
Czech Republic 131 42 34 55 1.055122e+07 17548.338213 10
Belarus 128 23 41 64 9.513000e+06 5740.456495 11
Jamaica 127 31 49 47 2.725941e+06 5232.024583 14
Croatia 125 50 40 35 4.224404e+06 11535.829356 10
Pakistan 121 42 45 34 1.889249e+08 1434.696665 9
Mexico 106 32 23 51 1.270172e+08 9005.024265 19
South Africa 106 30 39 37 5.495692e+07 5723.973357 17
Kenya 93 28 35 30 4.605030e+07 1376.712829 11
Turkey 86 38 25 23 7.866583e+07 9125.687590 16
Nigeria 84 19 38 27 1.822020e+08 2640.290739 8
Uruguay 76 44 2 30 3.431555e+06 15573.900919 8
Iran 61 16 21 24 7.910927e+07 NaN 15
In [78]:
medals_per_country.rank(ascending = False).head(50)
Out[78]:
Total Gold Silver Bronze Population GDP Total_Games
Country
United States 1.0 1.0 1.0 1.0 3.0 7.0 1.0
Soviet Union 2.0 2.0 2.0 2.0 NaN NaN 28.0
United Kingdom 3.0 4.0 3.0 3.0 20.0 13.0 10.0
Germany 4.0 3.0 5.0 5.0 15.0 18.0 19.0
France 5.0 8.0 4.0 4.0 19.0 22.0 3.0
Italy 6.0 5.0 7.0 7.0 21.0 25.0 9.0
Sweden 7.0 6.0 6.0 6.0 71.0 10.0 3.0
Canada 8.0 7.0 8.0 12.0 35.0 15.0 3.0
Australia 9.0 13.0 9.0 8.0 46.0 6.0 14.0
Hungary 10.0 9.0 14.0 13.0 70.0 48.0 13.0
Russia 11.0 12.0 11.0 11.0 8.0 57.0 45.0
Norway 12.0 11.0 10.0 17.0 90.0 3.0 8.0
East Germany 13.0 10.0 13.0 16.0 NaN NaN 45.0
Netherlands 14.0 15.0 15.0 10.0 55.0 12.0 11.0
Finland 15.0 18.0 17.0 9.0 88.0 17.0 6.0
China 16.0 14.0 12.0 19.0 1.0 63.0 34.5
Japan 17.0 16.0 16.0 14.0 10.0 24.0 12.0
Switzerland 18.0 22.0 18.0 18.0 78.0 2.0 6.0
Romania 19.0 21.0 23.0 15.0 51.0 59.0 24.0
Korea, South 20.0 17.0 19.0 24.0 24.0 27.0 20.0
West Germany 21.0 19.0 24.0 21.0 NaN NaN 40.0
Poland 22.0 27.0 25.0 20.0 33.0 47.0 17.0
Denmark 23.0 23.0 22.0 25.0 86.0 9.0 17.0
Czechoslovakia 24.0 32.0 21.0 23.0 NaN NaN 17.0
Spain 25.0 29.0 20.0 30.0 26.0 28.0 22.0
Yugoslavia 26.0 24.0 26.0 29.0 NaN NaN 28.0
Brazil 27.0 34.0 28.0 22.0 5.0 60.0 28.0
Austria 28.0 28.0 27.0 27.0 75.0 14.0 6.0
Belgium 29.0 30.0 29.0 26.0 62.0 20.0 15.0
Cuba 30.0 20.0 31.0 31.0 61.0 NaN 36.5
Bulgaria 31.0 36.0 30.0 28.0 81.0 64.0 24.0
Unified Team 32.0 25.5 34.0 35.0 NaN NaN 107.5
Unified Team of Germany 33.0 33.0 32.0 32.0 NaN NaN 73.0
Argentina 34.0 35.0 33.0 34.0 29.0 44.0 28.0
New Zealand 35.0 31.0 45.0 36.0 93.0 21.0 21.0
India 36.5 25.5 52.0 43.0 2.0 100.0 31.5
Ukraine 36.5 38.0 38.0 33.0 28.0 96.0 57.0
Greece 38.0 43.0 35.0 39.0 65.0 35.0 28.0
Czech Republic 39.0 40.5 44.0 38.0 67.0 36.0 50.5
Belarus 40.0 48.5 39.0 37.0 73.0 71.0 45.0
Jamaica 41.0 45.0 36.0 41.0 104.0 75.0 36.5
Croatia 42.0 37.0 40.0 45.0 94.0 49.0 50.5
Pakistan 43.0 40.5 37.0 46.0 6.0 101.0 57.0
Mexico 44.5 44.0 48.0 40.0 9.0 58.0 24.0
South Africa 44.5 46.0 41.0 44.0 22.0 72.0 31.5
Kenya 46.0 47.0 43.0 48.5 27.0 103.0 45.0
Turkey 47.0 42.0 47.0 54.5 17.0 56.0 33.0
Nigeria 48.0 53.0 42.0 51.0 7.0 93.0 64.0
Uruguay 49.0 39.0 94.5 48.5 100.0 40.0 64.0
Iran 50.0 55.0 49.0 53.0 16.0 NaN 34.5

Statistical Analysis and Hypothesis Testing with scipy¶

In the follwing work with Ranks! Check whether GDP (Standard of Living), Total_Games (Political Stability measure) and Population (Size) have an effect on Total Medals. (hint: work with spearman correlation, not with pearson correlation)

In [79]:
medals_per_country
Out[79]:
Total Gold Silver Bronze Population GDP Total_Games
Country
United States 5238 2402 1571 1265 321418820.0 56115.718426 48
Soviet Union 2489 1088 724 677 NaN NaN 18
United Kingdom 1799 580 632 587 65138232.0 43875.969614 42
Germany 1665 589 504 572 81413145.0 41313.313995 25
France 1548 444 526 578 66808385.0 36205.568102 47
... ... ... ... ... ... ... ...
Iraq 1 0 0 1 36423395.0 4943.760388 1
Macedonia 1 0 0 1 2078453.0 4852.657848 1
Mauritius 1 0 0 1 1262605.0 9252.110724 1
Niger 1 0 0 1 19899120.0 358.958152 1
Togo 1 0 0 1 7304578.0 559.635877 1

145 rows × 7 columns

In [80]:
medals_per_country.drop(columns = ["Gold", "Silver", "Bronze"], inplace = True)
In [81]:
medals_per_country.corr(method = "pearson")
Out[81]:
Total Population GDP Total_Games
Total 1.000000 0.206606 0.437131 0.712917
Population 0.206606 1.000000 -0.089976 0.123700
GDP 0.437131 -0.089976 1.000000 0.563540
Total_Games 0.712917 0.123700 0.563540 1.000000
In [82]:
medals_per_country.corr(method = "spearman")
Out[82]:
Total Population GDP Total_Games
Total 1.000000 0.419755 0.458478 0.927611
Population 0.419755 1.000000 -0.239097 0.402098
GDP 0.458478 -0.239097 1.000000 0.498056
Total_Games 0.927611 0.402098 0.498056 1.000000
In [83]:
medals_per_country.rank(ascending = False).corr(method = "pearson")
Out[83]:
Total Population GDP Total_Games
Total 1.000000 0.419634 0.458508 0.927611
Population 0.419634 1.000000 -0.239567 0.401448
GDP 0.458508 -0.239567 1.000000 0.497109
Total_Games 0.927611 0.401448 0.497109 1.000000
In [84]:
import scipy.stats as stats

Hypothesis 1: There is no relationship between Total Medals and Population

In [85]:
stats.spearmanr(medals_per_country.Total, medals_per_country.Population, 
                             nan_policy = "omit")
Out[85]:
SignificanceResult(statistic=0.41975527887063924, pvalue=8.132240868116897e-07)
In [86]:
r, p_value = stats.spearmanr(medals_per_country.Total, medals_per_country.Population, 
                             nan_policy = "omit")
In [87]:
r
Out[87]:
0.41975527887063924
In [88]:
round(p_value)
Out[88]:
0

Reject Hypothesis 1 -> There is a significant (positive) relationship between Total Medals and Population

Hypothesis 2: There is no relationship between Total Medals and GDP per Capita

In [89]:
r, p_value = stats.spearmanr(medals_per_country.Total, medals_per_country.GDP,
                             nan_policy = "omit")
In [90]:
r
Out[90]:
0.45847787983426685
In [91]:
p_value
Out[91]:
1.5758169913239602e-07

Reject Hypothesis 2 -> There is a significant (positive) relationship between Total Medals and GDP per Capita

Hypothesis 3: There is no relationship between Total Medals and Participations

In [92]:
medals_per_country.head(50)
Out[92]:
Total Population GDP Total_Games
Country
United States 5238 3.214188e+08 56115.718426 48
Soviet Union 2489 NaN NaN 18
United Kingdom 1799 6.513823e+07 43875.969614 42
Germany 1665 8.141314e+07 41313.313995 25
France 1548 6.680838e+07 36205.568102 47
Italy 1488 6.080208e+07 29957.804315 43
Sweden 1477 9.798871e+06 50579.673649 47
Canada 1274 3.585177e+07 43248.529909 47
Australia 1204 2.378117e+07 56310.962993 30
Hungary 1091 9.844686e+06 12363.543460 31
Russia 1031 1.440968e+08 9092.580536 11
Norway 1011 5.195921e+06 74400.369777 44
East Germany 987 NaN NaN 11
Netherlands 973 1.693652e+07 44299.768085 39
Finland 890 5.482013e+06 42311.036231 46
China 889 1.371220e+09 8027.683810 15
Japan 851 1.269585e+08 32477.215145 32
Switzerland 665 8.286976e+06 80945.079219 46
Romania 642 1.983239e+07 8972.922518 19
Korea, South 616 5.061704e+07 27221.524051 22
West Germany 584 NaN NaN 12
Poland 538 3.799949e+07 12554.547554 27
Denmark 512 5.676002e+06 51989.293471 27
Czechoslovakia 487 NaN NaN 27
Spain 444 4.641827e+07 25831.582305 20
Yugoslavia 442 NaN NaN 18
Brazil 431 2.078475e+08 8538.589975 18
Austria 426 8.611088e+06 43774.985174 46
Belgium 424 1.128572e+07 40324.027766 28
Cuba 410 1.138956e+07 NaN 14
Bulgaria 339 7.177991e+06 6993.477360 19
Unified Team 283 NaN NaN 2
Unified Team of Germany 281 NaN NaN 6
Argentina 259 4.341676e+07 13431.878340 18
New Zealand 191 4.595700e+06 37807.967276 21
India 184 1.311051e+09 1598.259034 17
Ukraine 184 4.519820e+07 2114.954716 9
Greece 148 1.082373e+07 18002.230578 18
Czech Republic 131 1.055122e+07 17548.338213 10
Belarus 128 9.513000e+06 5740.456495 11
Jamaica 127 2.725941e+06 5232.024583 14
Croatia 125 4.224404e+06 11535.829356 10
Pakistan 121 1.889249e+08 1434.696665 9
Mexico 106 1.270172e+08 9005.024265 19
South Africa 106 5.495692e+07 5723.973357 17
Kenya 93 4.605030e+07 1376.712829 11
Turkey 86 7.866583e+07 9125.687590 16
Nigeria 84 1.822020e+08 2640.290739 8
Uruguay 76 3.431555e+06 15573.900919 8
Iran 61 7.910927e+07 NaN 15
In [93]:
r, p_value = stats.spearmanr(medals_per_country.head(50).Total, 
                             medals_per_country.head(50).Total_Games)
In [94]:
r
Out[94]:
0.6818689415046658
In [95]:
p_value
Out[95]:
4.982151209468708e-08

Reject Hypothesis 3 -> There is a significant (positive) relationship between Total Medals and Participations

In [ ]:

Aggregating and Ranking¶

Create the following Seaborn Heatmap with Medal Ranks for Top 50 Countries (Total Medals, Summer Games Medals, Winter Games Medals, Men, Women).

image.png

In [96]:
olympics
Out[96]:
Edition Year City Sport Discipline Athlete Code Gender Event Medal Country Games
0 Summer 1896 Athens Aquatics Swimming HAJOS, Alfred HUN Men 100M Freestyle Gold Hungary 1896 Athens
1 Summer 1896 Athens Aquatics Swimming HERSCHMANN, Otto AUT Men 100M Freestyle Silver Austria 1896 Athens
2 Summer 1896 Athens Aquatics Swimming DRIVAS, Dimitrios GRE Men 100M Freestyle For Sailors Bronze Greece 1896 Athens
3 Summer 1896 Athens Aquatics Swimming MALOKINIS, Ioannis GRE Men 100M Freestyle For Sailors Gold Greece 1896 Athens
4 Summer 1896 Athens Aquatics Swimming CHASAPIS, Spiridon GRE Men 100M Freestyle For Sailors Silver Greece 1896 Athens
... ... ... ... ... ... ... ... ... ... ... ... ...
36926 Winter 2014 Sochi Skiing Snowboard JONES, Jenny GBR Women Slopestyle Bronze United Kingdom 2014 Sochi
36927 Winter 2014 Sochi Skiing Snowboard ANDERSON, Jamie USA Women Slopestyle Gold United States 2014 Sochi
36928 Winter 2014 Sochi Skiing Snowboard MALTAIS, Dominique CAN Women Snowboard Cross Silver Canada 2014 Sochi
36929 Winter 2014 Sochi Skiing Snowboard SAMKOVA, Eva CZE Women Snowboard Cross Gold Czech Republic 2014 Sochi
36930 Winter 2014 Sochi Skiing Snowboard TRESPEUCH, Chloe FRA Women Snowboard Cross Bronze France 2014 Sochi

36931 rows × 12 columns

In [97]:
sum_vs_win = pd.crosstab(olympics.Country, olympics.Edition)
In [98]:
sum_vs_win
Out[98]:
Edition Summer Winter
Country
Afghanistan 2 0
Algeria 15 0
Argentina 259 0
Armenia 11 0
Australasia 29 0
... ... ...
West Germany 490 94
West Indies Federation 5 0
Yugoslavia 435 7
Zambia 2 0
Zimbabwe 23 0

145 rows × 2 columns

In [99]:
men_vs_women =  pd.crosstab(olympics.Country, olympics.Gender)
men_vs_women
Out[99]:
Gender Men Women
Country
Afghanistan 2 0
Algeria 12 3
Argentina 187 72
Armenia 11 0
Australasia 27 2
... ... ...
West Germany 450 134
West Indies Federation 5 0
Yugoslavia 379 63
Zambia 2 0
Zimbabwe 0 23

145 rows × 2 columns

In [100]:
medals_by_cats = pd.concat([sum_vs_win, men_vs_women], axis = 1)
medals_by_cats
Out[100]:
Summer Winter Men Women
Country
Afghanistan 2 0 2 0
Algeria 15 0 12 3
Argentina 259 0 187 72
Armenia 11 0 11 0
Australasia 29 0 27 2
... ... ... ... ...
West Germany 490 94 450 134
West Indies Federation 5 0 5 0
Yugoslavia 435 7 379 63
Zambia 2 0 2 0
Zimbabwe 23 0 0 23

145 rows × 4 columns

In [101]:
medals_by_cats["Total"] = medals_by_cats.Summer + medals_by_cats.Winter
In [102]:
medals_by_cats
Out[102]:
Summer Winter Men Women Total
Country
Afghanistan 2 0 2 0 2
Algeria 15 0 12 3 15
Argentina 259 0 187 72 259
Armenia 11 0 11 0 11
Australasia 29 0 27 2 29
... ... ... ... ... ...
West Germany 490 94 450 134 584
West Indies Federation 5 0 5 0 5
Yugoslavia 435 7 379 63 442
Zambia 2 0 2 0 2
Zimbabwe 23 0 0 23 23

145 rows × 5 columns

In [103]:
medals_by_cats.sort_values("Total", ascending = False, inplace = True)
In [104]:
medals_by_cats
Out[104]:
Summer Winter Men Women Total
Country
United States 4585 653 3618 1620 5238
Soviet Union 2049 440 1807 682 2489
United Kingdom 1720 79 1470 329 1799
Germany 1305 360 1119 546 1665
France 1396 152 1351 197 1548
... ... ... ... ... ...
Iraq 1 0 1 0 1
Senegal 1 0 1 0 1
Macedonia 1 0 1 0 1
Mauritius 1 0 1 0 1
Guatemala 1 0 1 0 1

145 rows × 5 columns

In [105]:
ranks = medals_by_cats.rank(ascending = False, method = "average")
ranks
Out[105]:
Summer Winter Men Women Total
Country
United States 1.0 1.0 1.0 1.0 1.0
Soviet Union 2.0 4.0 2.0 2.0 2.0
United Kingdom 3.0 19.0 3.0 11.0 3.0
Germany 5.0 7.0 7.0 4.0 4.0
France 4.0 14.0 4.0 16.5 5.0
... ... ... ... ... ...
Iraq 132.5 95.5 127.0 120.0 133.5
Senegal 132.5 95.5 127.0 120.0 133.5
Macedonia 132.5 95.5 127.0 120.0 133.5
Mauritius 132.5 95.5 127.0 120.0 133.5
Guatemala 132.5 95.5 127.0 120.0 133.5

145 rows × 5 columns

In [106]:
top_50 = ranks.head(50)
In [107]:
plt.figure(figsize = (50, 10))
sns.heatmap(top_50.T,cmap='RdYlGn_r',annot=True,fmt='2.0f')
plt.show()
In [ ]:

Summer Games vs. Winter Games - does Geographical Location matter?¶

Identify Countries that are

  1. equally successful in Summer and Winter Games
  2. more successful in Summer Games
  3. more successful in Winter Games

What could be the reasons?

In [108]:
top_50.sort_values("Summer")
Out[108]:
Summer Winter Men Women Total
Country
United States 1.0 1.0 1.0 1.0 1.0
Soviet Union 2.0 4.0 2.0 2.0 2.0
United Kingdom 3.0 19.0 3.0 11.0 3.0
France 4.0 14.0 4.0 16.5 5.0
Germany 5.0 7.0 7.0 4.0 4.0
Italy 6.0 11.0 5.0 18.0 6.0
Australia 7.0 27.0 12.0 5.0 9.0
Hungary 8.0 30.0 8.0 14.0 10.0
Sweden 9.0 6.0 6.0 16.5 7.0
Netherlands 10.0 15.0 14.0 9.0 14.0
East Germany 11.0 12.0 17.0 8.0 13.0
China 12.0 18.0 30.0 3.0 16.0
Japan 13.0 21.0 13.0 13.0 17.0
Russia 14.0 10.0 16.0 6.0 11.0
Canada 15.0 2.0 9.0 7.0 8.0
Romania 16.0 41.5 28.0 10.0 19.0
Norway 17.0 3.0 10.0 15.0 12.0
Korea, South 18.0 17.0 25.5 12.0 20.0
Poland 19.0 23.0 19.0 24.0 22.0
Denmark 20.0 38.5 20.0 29.5 23.0
West Germany 21.0 16.0 18.0 19.0 21.0
Finland 22.0 5.0 11.0 23.0 15.0
Spain 23.0 41.5 24.0 25.0 25.0
Yugoslavia 24.0 35.0 23.0 36.0 26.0
Brazil 25.0 95.5 29.0 20.0 27.0
Belgium 26.0 29.0 21.0 43.0 29.0
Cuba 27.0 95.5 27.0 26.0 30.0
Switzerland 28.0 8.0 15.0 28.0 18.0
Bulgaria 29.0 37.0 32.0 21.0 31.0
Czechoslovakia 30.0 13.0 22.0 31.0 24.0
Unified Team of Germany 31.0 24.0 31.0 35.0 33.0
Argentina 32.0 95.5 33.0 32.0 34.0
Unified Team 33.0 22.0 35.0 22.0 32.0
New Zealand 34.0 44.5 36.0 39.0 35.0
India 35.0 95.5 34.0 71.5 36.5
Ukraine 36.0 31.5 42.0 27.0 36.5
Greece 37.0 95.5 39.0 37.0 38.0
Austria 38.0 9.0 25.5 29.5 28.0
Jamaica 39.0 95.5 50.0 33.5 41.0
Pakistan 40.0 95.5 37.0 120.0 43.0
Croatia 41.0 31.5 38.0 53.5 42.0
Belarus 42.0 27.0 49.0 33.5 40.0
Mexico 43.5 95.5 41.0 46.0 44.5
South Africa 43.5 95.5 43.0 42.0 44.5
Kenya 45.0 95.5 44.0 51.5 46.0
Turkey 46.0 95.5 45.0 58.5 47.0
Nigeria 47.0 95.5 47.0 44.5 48.0
Uruguay 48.0 95.5 46.0 120.0 49.0
Iran 49.0 95.5 48.0 120.0 50.0
Czech Republic 51.0 20.0 40.0 38.0 39.0
In [109]:
top_50.sort_values("Winter")
Out[109]:
Summer Winter Men Women Total
Country
United States 1.0 1.0 1.0 1.0 1.0
Canada 15.0 2.0 9.0 7.0 8.0
Norway 17.0 3.0 10.0 15.0 12.0
Soviet Union 2.0 4.0 2.0 2.0 2.0
Finland 22.0 5.0 11.0 23.0 15.0
Sweden 9.0 6.0 6.0 16.5 7.0
Germany 5.0 7.0 7.0 4.0 4.0
Switzerland 28.0 8.0 15.0 28.0 18.0
Austria 38.0 9.0 25.5 29.5 28.0
Russia 14.0 10.0 16.0 6.0 11.0
Italy 6.0 11.0 5.0 18.0 6.0
East Germany 11.0 12.0 17.0 8.0 13.0
Czechoslovakia 30.0 13.0 22.0 31.0 24.0
France 4.0 14.0 4.0 16.5 5.0
Netherlands 10.0 15.0 14.0 9.0 14.0
West Germany 21.0 16.0 18.0 19.0 21.0
Korea, South 18.0 17.0 25.5 12.0 20.0
China 12.0 18.0 30.0 3.0 16.0
United Kingdom 3.0 19.0 3.0 11.0 3.0
Czech Republic 51.0 20.0 40.0 38.0 39.0
Japan 13.0 21.0 13.0 13.0 17.0
Unified Team 33.0 22.0 35.0 22.0 32.0
Poland 19.0 23.0 19.0 24.0 22.0
Unified Team of Germany 31.0 24.0 31.0 35.0 33.0
Belarus 42.0 27.0 49.0 33.5 40.0
Australia 7.0 27.0 12.0 5.0 9.0
Belgium 26.0 29.0 21.0 43.0 29.0
Hungary 8.0 30.0 8.0 14.0 10.0
Ukraine 36.0 31.5 42.0 27.0 36.5
Croatia 41.0 31.5 38.0 53.5 42.0
Yugoslavia 24.0 35.0 23.0 36.0 26.0
Bulgaria 29.0 37.0 32.0 21.0 31.0
Denmark 20.0 38.5 20.0 29.5 23.0
Spain 23.0 41.5 24.0 25.0 25.0
Romania 16.0 41.5 28.0 10.0 19.0
New Zealand 34.0 44.5 36.0 39.0 35.0
India 35.0 95.5 34.0 71.5 36.5
Cuba 27.0 95.5 27.0 26.0 30.0
Greece 37.0 95.5 39.0 37.0 38.0
Brazil 25.0 95.5 29.0 20.0 27.0
Uruguay 48.0 95.5 46.0 120.0 49.0
Jamaica 39.0 95.5 50.0 33.5 41.0
Pakistan 40.0 95.5 37.0 120.0 43.0
Mexico 43.5 95.5 41.0 46.0 44.5
South Africa 43.5 95.5 43.0 42.0 44.5
Kenya 45.0 95.5 44.0 51.5 46.0
Turkey 46.0 95.5 45.0 58.5 47.0
Nigeria 47.0 95.5 47.0 44.5 48.0
Argentina 32.0 95.5 33.0 32.0 34.0
Iran 49.0 95.5 48.0 120.0 50.0
In [110]:
plt.figure(figsize = (50, 10))
sns.heatmap(top_50.sort_values(by = "Summer").T,cmap='RdYlGn_r',annot=True,fmt='2.0f')
plt.show()
In [111]:
plt.figure(figsize = (50, 10))
sns.heatmap(top_50.sort_values(by = "Winter").T,cmap='RdYlGn_r',annot=True,fmt='2.0f')
plt.show()
In [112]:
top_50.Summer.sub(top_50.Winter).sort_values()
Out[112]:
Country
Brazil                    -70.5
Cuba                      -68.5
Argentina                 -63.5
India                     -60.5
Greece                    -58.5
Jamaica                   -56.5
Pakistan                  -55.5
South Africa              -52.0
Mexico                    -52.0
Kenya                     -50.5
Turkey                    -49.5
Nigeria                   -48.5
Uruguay                   -47.5
Iran                      -46.5
Romania                   -25.5
Hungary                   -22.0
Australia                 -20.0
Denmark                   -18.5
Spain                     -18.5
United Kingdom            -16.0
Yugoslavia                -11.0
New Zealand               -10.5
France                    -10.0
Japan                      -8.0
Bulgaria                   -8.0
China                      -6.0
Netherlands                -5.0
Italy                      -5.0
Poland                     -4.0
Belgium                    -3.0
Soviet Union               -2.0
Germany                    -2.0
East Germany               -1.0
United States               0.0
Korea, South                1.0
Sweden                      3.0
Russia                      4.0
Ukraine                     4.5
West Germany                5.0
Unified Team of Germany     7.0
Croatia                     9.5
Unified Team               11.0
Canada                     13.0
Norway                     14.0
Belarus                    15.0
Finland                    17.0
Czechoslovakia             17.0
Switzerland                20.0
Austria                    29.0
Czech Republic             31.0
dtype: float64
In [113]:
rank_diff = top_50.Summer.sub(top_50.Winter).sort_values().to_frame()
rank_diff
Out[113]:
0
Country
Brazil -70.5
Cuba -68.5
Argentina -63.5
India -60.5
Greece -58.5
Jamaica -56.5
Pakistan -55.5
South Africa -52.0
Mexico -52.0
Kenya -50.5
Turkey -49.5
Nigeria -48.5
Uruguay -47.5
Iran -46.5
Romania -25.5
Hungary -22.0
Australia -20.0
Denmark -18.5
Spain -18.5
United Kingdom -16.0
Yugoslavia -11.0
New Zealand -10.5
France -10.0
Japan -8.0
Bulgaria -8.0
China -6.0
Netherlands -5.0
Italy -5.0
Poland -4.0
Belgium -3.0
Soviet Union -2.0
Germany -2.0
East Germany -1.0
United States 0.0
Korea, South 1.0
Sweden 3.0
Russia 4.0
Ukraine 4.5
West Germany 5.0
Unified Team of Germany 7.0
Croatia 9.5
Unified Team 11.0
Canada 13.0
Norway 14.0
Belarus 15.0
Finland 17.0
Czechoslovakia 17.0
Switzerland 20.0
Austria 29.0
Czech Republic 31.0
In [114]:
plt.figure(figsize = (35, 5))
sns.heatmap(rank_diff.T,cmap='RdBu',annot=True,fmt='2.0f', center = 0)
plt.show()
In [ ]:

Men vs. Women - does Culture & Religion matter?¶

Identify Countries where

  1. Men and Women are equally successful
  2. Men are more successful
  3. Women are more successful

What could be the reasons?

In [115]:
plt.figure(figsize = (50, 10))
sns.heatmap(top_50.sort_values(by = "Men").T,cmap='RdYlGn_r',annot=True,fmt='2.0f')
plt.show()
In [116]:
plt.figure(figsize = (50, 10))
sns.heatmap(top_50.sort_values(by = "Women").T,cmap='RdYlGn_r',annot=True,fmt='2.0f')
plt.show()
In [117]:
rank_diff2 = top_50.Men.sub(top_50.Women).sort_values().to_frame()
rank_diff2
Out[117]:
0
Country
Pakistan -83.0
Uruguay -74.0
Iran -72.0
India -37.5
Belgium -22.0
Croatia -15.5
Turkey -13.5
Yugoslavia -13.0
Italy -13.0
Switzerland -13.0
France -12.5
Finland -12.0
Sweden -10.5
Denmark -9.5
Czechoslovakia -9.0
United Kingdom -8.0
Kenya -7.5
Hungary -6.0
Mexico -5.0
Poland -5.0
Norway -5.0
Unified Team of Germany -4.0
Austria -4.0
New Zealand -3.0
Spain -1.0
West Germany -1.0
United States 0.0
Soviet Union 0.0
Japan 0.0
South Africa 1.0
Cuba 1.0
Argentina 1.0
Canada 2.0
Czech Republic 2.0
Greece 2.0
Nigeria 2.5
Germany 3.0
Netherlands 5.0
Australia 7.0
East Germany 9.0
Brazil 9.0
Russia 10.0
Bulgaria 11.0
Unified Team 13.0
Korea, South 13.5
Ukraine 15.0
Belarus 15.5
Jamaica 16.5
Romania 18.0
China 27.0
In [118]:
plt.figure(figsize = (35, 5))
sns.heatmap(rank_diff2.T,cmap='RdBu_r',annot=True,fmt='2.0f', center = 0)
plt.show()
In [ ]:

Do Traditions matter?¶

Create the following Seaborn Heatmap that shows the Ranks of Top 50 Countries by Sports.
Identify traditional Sports / National Sports for e.g. UK and China!

image.png

In [119]:
olympics
Out[119]:
Edition Year City Sport Discipline Athlete Code Gender Event Medal Country Games
0 Summer 1896 Athens Aquatics Swimming HAJOS, Alfred HUN Men 100M Freestyle Gold Hungary 1896 Athens
1 Summer 1896 Athens Aquatics Swimming HERSCHMANN, Otto AUT Men 100M Freestyle Silver Austria 1896 Athens
2 Summer 1896 Athens Aquatics Swimming DRIVAS, Dimitrios GRE Men 100M Freestyle For Sailors Bronze Greece 1896 Athens
3 Summer 1896 Athens Aquatics Swimming MALOKINIS, Ioannis GRE Men 100M Freestyle For Sailors Gold Greece 1896 Athens
4 Summer 1896 Athens Aquatics Swimming CHASAPIS, Spiridon GRE Men 100M Freestyle For Sailors Silver Greece 1896 Athens
... ... ... ... ... ... ... ... ... ... ... ... ...
36926 Winter 2014 Sochi Skiing Snowboard JONES, Jenny GBR Women Slopestyle Bronze United Kingdom 2014 Sochi
36927 Winter 2014 Sochi Skiing Snowboard ANDERSON, Jamie USA Women Slopestyle Gold United States 2014 Sochi
36928 Winter 2014 Sochi Skiing Snowboard MALTAIS, Dominique CAN Women Snowboard Cross Silver Canada 2014 Sochi
36929 Winter 2014 Sochi Skiing Snowboard SAMKOVA, Eva CZE Women Snowboard Cross Gold Czech Republic 2014 Sochi
36930 Winter 2014 Sochi Skiing Snowboard TRESPEUCH, Chloe FRA Women Snowboard Cross Bronze France 2014 Sochi

36931 rows × 12 columns

In [120]:
olympics.Sport.value_counts()
Out[120]:
Aquatics             4170
Athletics            3637
Rowing               2667
Gymnastics           2307
Skiing               1781
Fencing              1613
Ice Hockey           1563
Football             1497
Hockey               1422
Skating              1256
Wrestling            1211
Shooting             1150
Sailing              1109
Cycling              1105
Basketball           1012
Canoe / Kayak        1002
Volleyball            994
Handball              973
Equestrian            939
Boxing                894
Weightlifting         591
Judo                  491
Bobsleigh             452
Biathlon              420
Baseball              335
Archery               329
Tennis                296
Rugby                 192
Softball              180
Luge                  180
Modern Pentathlon     180
Curling               172
Badminton             144
Table Tennis          144
Taekwondo             112
Tug of War             94
Canoe                  81
Polo                   66
Lacrosse               59
Golf                   30
Triathlon              24
Cricket                24
Rackets                10
Croquet                 8
Water Motorsports       5
Basque Pelota           4
Roque                   3
Jeu de paume            3
Name: Sport, dtype: int64
In [121]:
sports = olympics.Sport.value_counts().index
sports
Out[121]:
Index(['Aquatics', 'Athletics', 'Rowing', 'Gymnastics', 'Skiing', 'Fencing',
       'Ice Hockey', 'Football', 'Hockey', 'Skating', 'Wrestling', 'Shooting',
       'Sailing', 'Cycling', 'Basketball', 'Canoe / Kayak', 'Volleyball',
       'Handball', 'Equestrian', 'Boxing', 'Weightlifting', 'Judo',
       'Bobsleigh', 'Biathlon', 'Baseball', 'Archery', 'Tennis', 'Rugby',
       'Softball', 'Luge', 'Modern Pentathlon', 'Curling', 'Badminton',
       'Table Tennis', 'Taekwondo', 'Tug of War', 'Canoe', 'Polo', 'Lacrosse',
       'Golf', 'Triathlon', 'Cricket', 'Rackets', 'Croquet',
       'Water Motorsports', 'Basque Pelota', 'Roque', 'Jeu de paume'],
      dtype='object')
In [122]:
olympics.Country.value_counts().head(20)
Out[122]:
United States     5238
Soviet Union      2489
United Kingdom    1799
Germany           1665
France            1548
Italy             1488
Sweden            1477
Canada            1274
Australia         1204
Hungary           1091
Russia            1031
Norway            1011
East Germany       987
Netherlands        973
Finland            890
China              889
Japan              851
Switzerland        665
Romania            642
Korea, South       616
Name: Country, dtype: int64
In [123]:
top_20 = olympics.Country.value_counts().head(20).index
top_20
Out[123]:
Index(['United States', 'Soviet Union', 'United Kingdom', 'Germany', 'France',
       'Italy', 'Sweden', 'Canada', 'Australia', 'Hungary', 'Russia', 'Norway',
       'East Germany', 'Netherlands', 'Finland', 'China', 'Japan',
       'Switzerland', 'Romania', 'Korea, South'],
      dtype='object')
In [124]:
by_sport = pd.crosstab(olympics.Country, olympics.Sport)
by_sport
Out[124]:
Sport Aquatics Archery Athletics Badminton Baseball Basketball Basque Pelota Biathlon Bobsleigh Boxing ... Softball Table Tennis Taekwondo Tennis Triathlon Tug of War Volleyball Water Motorsports Weightlifting Wrestling
Country
Afghanistan 0 0 0 0 0 0 0 0 0 0 ... 0 0 2 0 0 0 0 0 0 0
Algeria 0 0 7 0 0 0 0 0 0 6 ... 0 0 0 0 0 0 0 0 0 0
Argentina 3 0 5 0 0 24 0 0 0 24 ... 0 0 1 6 0 0 12 0 2 0
Armenia 0 0 0 0 0 0 0 0 0 1 ... 0 0 0 0 0 0 0 0 4 6
Australasia 11 0 1 0 0 0 0 0 0 1 ... 0 0 0 1 0 0 0 0 0 0
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
West Germany 62 0 67 0 0 0 0 14 22 6 ... 0 0 0 3 0 0 0 0 7 9
West Indies Federation 0 0 5 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
Yugoslavia 91 0 2 0 0 96 0 0 0 11 ... 0 4 0 0 0 0 24 0 0 16
Zambia 0 0 1 0 0 0 0 0 0 1 ... 0 0 0 0 0 0 0 0 0 0
Zimbabwe 7 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0

145 rows × 48 columns

In [125]:
by_sport = by_sport.rank(ascending = False, method = "average")
by_sport
Out[125]:
Sport Aquatics Archery Athletics Badminton Baseball Basketball Basque Pelota Biathlon Bobsleigh Boxing ... Softball Table Tennis Taekwondo Tennis Triathlon Tug of War Volleyball Water Motorsports Weightlifting Wrestling
Country
Afghanistan 101.0 84.0 122.0 78.0 76.0 83.0 74.0 84.5 80.5 112.0 ... 75.0 79.0 19.5 90.5 79.0 76.5 86.0 74.0 103.5 103.0
Algeria 101.0 84.0 47.0 78.0 76.0 83.0 74.0 84.5 80.5 35.0 ... 75.0 79.0 89.5 90.5 79.0 76.5 86.0 74.0 103.5 103.0
Argentina 41.0 84.0 53.5 78.0 76.0 11.5 74.0 84.5 80.5 9.0 ... 75.0 79.0 28.0 12.0 79.0 76.5 17.0 74.0 45.5 103.0
Armenia 101.0 84.0 122.0 78.0 76.0 83.0 74.0 84.5 80.5 70.5 ... 75.0 79.0 89.5 90.5 79.0 76.5 86.0 74.0 35.5 40.0
Australasia 30.5 84.0 88.0 78.0 76.0 83.0 74.0 84.5 80.5 70.5 ... 75.0 79.0 89.5 31.0 79.0 76.5 86.0 74.0 103.5 103.0
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
West Germany 18.0 84.0 16.0 78.0 76.0 83.0 74.0 9.0 6.5 35.0 ... 75.0 79.0 89.5 21.0 79.0 76.5 86.0 74.0 28.5 32.0
West Indies Federation 101.0 84.0 53.5 78.0 76.0 83.0 74.0 84.5 80.5 112.0 ... 75.0 79.0 89.5 90.5 79.0 76.5 86.0 74.0 103.5 103.0
Yugoslavia 16.0 84.0 70.0 78.0 76.0 3.0 74.0 84.5 80.5 25.5 ... 75.0 5.5 89.5 90.5 79.0 76.5 12.0 74.0 103.5 20.0
Zambia 101.0 84.0 88.0 78.0 76.0 83.0 74.0 84.5 80.5 70.5 ... 75.0 79.0 89.5 90.5 79.0 76.5 86.0 74.0 103.5 103.0
Zimbabwe 33.0 84.0 122.0 78.0 76.0 83.0 74.0 84.5 80.5 112.0 ... 75.0 79.0 89.5 90.5 79.0 76.5 86.0 74.0 103.5 103.0

145 rows × 48 columns

In [126]:
by_sport = by_sport.loc[top_20, sports].copy()
by_sport
Out[126]:
Aquatics Athletics Rowing Gymnastics Skiing Fencing Ice Hockey Football Hockey Skating ... Lacrosse Golf Triathlon Cricket Rackets Croquet Water Motorsports Basque Pelota Roque Jeu de paume
United States 1.0 1.0 1.0 2.0 8.0 7.0 2.0 1.0 12.0 1.0 ... 3.0 1.0 9.5 74.0 73.5 73.5 74.0 74.0 1.0 2.0
Soviet Union 4.0 3.0 7.0 1.0 5.0 4.0 5.0 3.0 11.0 4.0 ... 74.5 74.5 79.0 74.0 73.5 73.5 74.0 74.0 73.5 74.0
United Kingdom 10.5 2.0 2.0 19.0 34.5 15.0 10.0 20.0 5.0 16.0 ... 2.0 2.0 5.0 1.5 1.0 73.5 1.0 74.0 73.5 1.0
Germany 5.0 7.0 5.0 16.0 6.0 6.0 13.0 7.5 4.0 9.0 ... 74.5 74.5 5.0 74.0 73.5 73.5 74.0 74.0 73.5 74.0
France 14.0 8.0 11.0 17.0 10.0 2.0 79.5 23.0 83.0 17.0 ... 74.5 74.5 79.0 1.5 73.5 1.0 2.0 1.5 73.5 74.0
Italy 12.0 14.0 8.0 8.5 9.0 1.0 79.5 7.5 83.0 11.5 ... 74.5 74.5 79.0 74.0 73.5 73.5 74.0 74.0 73.5 74.0
Sweden 15.0 9.0 30.0 5.0 4.0 12.0 3.0 13.5 83.0 15.0 ... 74.5 74.5 9.5 74.0 73.5 73.5 74.0 74.0 73.5 74.0
Canada 13.0 12.5 3.0 30.5 12.0 90.0 1.0 22.0 83.0 2.0 ... 1.0 3.0 5.0 74.0 73.5 73.5 74.0 74.0 73.5 74.0
Australia 2.0 12.5 9.0 37.0 21.5 90.0 79.5 90.5 2.0 24.0 ... 74.5 74.5 1.0 74.0 73.5 73.5 74.0 74.0 73.5 74.0
Hungary 3.0 18.5 32.0 7.0 91.5 3.0 79.5 4.0 83.0 20.0 ... 74.5 74.5 79.0 74.0 73.5 73.5 74.0 74.0 73.5 74.0
Russia 8.0 10.0 23.5 8.5 11.0 8.5 9.0 90.5 83.0 7.0 ... 74.5 74.5 79.0 74.0 73.5 73.5 74.0 74.0 73.5 74.0
Norway 46.5 34.0 15.0 10.0 1.0 31.5 79.5 10.0 83.0 5.5 ... 74.5 74.5 79.0 74.0 73.5 73.5 74.0 74.0 73.5 74.0
East Germany 9.0 4.0 4.0 13.0 14.0 31.5 79.5 9.0 83.0 10.0 ... 74.5 74.5 79.0 74.0 73.5 73.5 74.0 74.0 73.5 74.0
Netherlands 10.5 31.5 10.0 26.0 34.5 15.0 79.5 16.5 1.0 3.0 ... 74.5 74.5 79.0 74.0 73.5 73.5 74.0 74.0 73.5 74.0
Finland 37.5 5.0 25.0 11.0 2.0 90.0 4.0 90.5 83.0 13.5 ... 74.5 74.5 79.0 74.0 73.5 73.5 74.0 74.0 73.5 74.0
China 6.0 28.5 22.0 6.0 25.0 13.0 79.5 33.0 14.5 8.0 ... 74.5 74.5 79.0 74.0 73.5 73.5 74.0 74.0 73.5 74.0
Japan 7.0 26.5 94.5 3.0 13.0 25.0 79.5 18.5 19.5 13.5 ... 74.5 74.5 79.0 74.0 73.5 73.5 74.0 74.0 73.5 74.0
Switzerland 53.5 45.0 13.0 14.0 7.0 17.0 7.0 25.0 83.0 28.0 ... 74.5 74.5 2.0 74.0 73.5 73.5 74.0 74.0 73.5 74.0
Romania 32.0 22.0 6.0 4.0 91.5 11.0 79.5 90.5 83.0 89.5 ... 74.5 74.5 79.0 74.0 73.5 73.5 74.0 74.0 73.5 74.0
Korea, South 37.5 70.0 94.5 28.5 91.5 18.0 79.5 29.5 10.0 5.5 ... 74.5 74.5 79.0 74.0 73.5 73.5 74.0 74.0 73.5 74.0

20 rows × 48 columns

In [127]:
plt.figure(figsize = (30, 10))
sns.heatmap(by_sport,cmap='RdYlGn_r', vmin = 1, vmax = 6, linewidth = 1)
plt.show()
In [ ]:
In [ ]:
In [ ]:
In [ ]:

Missing full Country Names (see Data Cleaning Part 1)¶

In [ ]:
{'URS': 'Soviet Union',
 'GDR': 'East Germany',
 'ROU': 'Romania',
 'FRG': 'West Germany',
 'TCH': 'Czechoslovakia',
 'YUG': 'Yugoslavia',
 'EUN': 'Unified Team',
 'EUA': 'Unified Team of Germany',
 'ZZX': 'Mixed teams',
 'SRB': 'Serbia',
 'ANZ': 'Australasia',
 'RU1': 'Russian Empire',
 'MNE': 'Montenegro',
 'TTO': 'Trinidad and Tobago',
 'BOH': 'Bohemia',
 'BWI': 'West Indies Federation',
 'SGP': 'Singapore',
 'IOP': 'Independent Olympic Participants'}